• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.427-429
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• 2005

In order to tune the speed controller in the linear servo applications the accurate information of a mover mass including a load mass is always required. This paper suggests the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) by using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. The application of the Least-Squares algorithm shows that the mass can be accurately estimated both in the simulation results and in the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3458-3481
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• 2021

Existing methods for blind identification of linear block codes without a candidate set are mainly built on the Gauss elimination process. However, the fault tolerance will fall short when the intercepted bit error rate (BER) is too high. To address this issue, we apply the reverse algebra approach and propose a novel "two-step-screening" algorithm by solving the linear error equations on the binary Galois field, or GF(2). In the first step, a recursive matrix partition is implemented to solve the system linear error equations where the coefficient matrix is constructed by the full codewords which come from the intercepted noisy bitstream. This process is repeated to derive all those possible parity-checks. In the second step, a check matrix constructed by the intercepted codewords is applied to find the correct parity-checks out of all possible parity-checks solutions. This novel "two-step-screening" algorithm can be used in different codes like Hamming codes, BCH codes, LDPC codes, and quasi-cyclic LDPC codes. The simulation results have shown that it can highly improve the fault tolerance ability compared to the existing Gauss elimination process-based algorithms.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.55-65
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• 2000

This paper presents a linear programming based algorithm for pattern classification. Pattern classification is being considered to be critical in the area of artificial intelligence and business applications. Previous methods employing linear programming have been aimed at two-group discrimination with one or more linear discriminant functions. Therefore, there are some limitations in applying available linear programming formulations directly to general multi-class classification problems. The algorithm proposed in this manuscript is based on quadratic or polynomial discriminant functions, which allow more flexibility in covering the class regions in the N-dimensional space. The proposed algorithm is compared with other competitive methods of pattern classification in experimental results and is shown to be competitive enough for a general purpose classifier.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.2
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• pp.159-163
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• 2006

In order to tune the speed controller in the linear servo applications an accurate information of a mover mass including a load mass is always required. This paper suggests the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) 4y using the parameter estimation method of Least-Squares algorithm. First, the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system is derived. Then the application of the Least-Squares algorithm shows that the mass can be accurately estimated both in the simulation results and in the experimental results.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.65-70
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• 1981

L.G. Khachiyan's algorithm for solving a system of strict (or open) linear inequalities with integral coefficients is described. This algorithm is based on the construction of a sequence of ellipsoids in R$^n$ of decreasing n-dimensional volume and contain-ing feasible region. The running time of the algorithm is polynomial in the number of bits of computer core memory required to store the coefficients. It can be applied to solve linear programming problems in polynomially bounded time by the duality theorem of the linear programming problem. But it is difficult to use in solving practical problems. Because it requires the computation of a square roots, besides other arithmatic operations, it is impossible to do these computations exactly with absolute precision.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.109-112
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• 2002

This paper presents relationship between the dynamic behavior of an asynchronous linear pipeline (ALP) and the performance of the ALP as buffers are allocated. Then the relationship is used in order to characterize a local optimum situation on the buffer design space of the ALP. Using the characterization we propose an efficient algorithm optimizing buffer allocation on an ALP in order to achieve its average case performance. Without the loss of optimality, our algorithm works in linear time complexity so it achieves fast buffer-configuration optimization. This paper makes two contributions. First, it describes relationship between the performance characteristics of an ALP and a local optimum on the buffer design space of the ALP. Second, it devises a buffer allocation algorithm finding an optimum solution in linear time complexity.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.256-262
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• 2013

This paper presents a linear path control algorithm for NeuroMate robot in a skull drilling system. For the path control inverse kinematics of the robot is analyzed and a linear interpolation algorithm is presented. A geometric approach is used for solving inverse kinematic equations for the robot. Four feasible solutions are found through the approach. The approach gives geometric insights for selecting the best solution from the feasible solutions. The presented linear interpolation algorithm computes a next position considering current velocity and remaining distance to the target position. Presented algorithm is implemented and tested in a skull drilling system.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.299-303
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• 2002

Many ocean color sensors are being operated at present and will be continued to operatein the coming years. However, these ocean color sensors have different spectral bands locations and higher level product algorithms. Thus the continuity of ocean color data from the satellite with different missions will be important for monitoring of oceanographic variation with long term research. In this study, CZCS band and algorithm are compared with OCTS and SeaWiFS algorithm for estimating chlorophyll. Missing bands of OCTS and CZCS for chlorophyll algorithm are estimated by linear-interpolation using SeaWiFS data. We were able to evaluate the effectiveness of the correction methods using linear interpolation method. Surprisingly, linear interpolation gave a better result than those of other bands.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.